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    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Declaring the Model"
      ],
      "metadata": {
        "id": "-Qc7b34LQnXm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "! pip install onnx\n",
        "! pip install onnxruntime\n",
        "! pip install optimum"
      ],
      "metadata": {
        "id": "XcF4X8ZS1Zi6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QXx9Y5z-Pbwd"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "\n",
        "class SimpleCNN(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(SimpleCNN, self).__init__()\n",
        "        self.conv1 = nn.Conv2d(3, 6, 5)   # Assuming input image channel=3 (RGB), 6 output channels, 5x5 kernel\n",
        "        self.pool = nn.MaxPool2d(2, 2)\n",
        "        self.conv2 = nn.Conv2d(6, 16, 5)\n",
        "        self.fc1 = nn.Linear(16 * 5 * 5, 120)   # 5*5 from image dimension\n",
        "        self.fc2 = nn.Linear(120, 84)\n",
        "        self.fc3 = nn.Linear(84, 10)  # Assuming 10 classes for output\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.pool(F.relu(self.conv1(x)))\n",
        "        x = self.pool(F.relu(self.conv2(x)))\n",
        "        x = x.view(-1, 16 * 5 * 5)  # Reshape before passing to fc layer\n",
        "        x = F.relu(self.fc1(x))\n",
        "        x = F.relu(self.fc2(x))\n",
        "        x = self.fc3(x)\n",
        "        return x\n",
        "\n",
        "# Initialize the model\n",
        "model = SimpleCNN()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Saving the state_dict"
      ],
      "metadata": {
        "id": "jQYMzsO6QrJG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Save model state_dict\n",
        "torch.save(model.state_dict(), \"simple_cnn_state_dict.pt\")\n"
      ],
      "metadata": {
        "id": "Zz9KaE7lQH5r"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Loading the Model\n",
        "1. You must \"Create a new model object\" before loading the state_dict"
      ],
      "metadata": {
        "id": "Z8_eBKAFQxbW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a new model object\n",
        "model2 = SimpleCNN()\n",
        "\n",
        "# Load the state_dict into the model\n",
        "model2.load_state_dict(torch.load(\"simple_cnn_state_dict.pt\"))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2u0VD3k7QNMz",
        "outputId": "0297fb4b-978a-4145-dbed-f1f1faa3daa8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<All keys matched successfully>"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Printing the State Dict"
      ],
      "metadata": {
        "id": "yMVlvO1TRGAm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Print model's state_dict\n",
        "print(\"Model's state_dict:\")\n",
        "for param_tensor in model2.state_dict():\n",
        "    print(param_tensor, \"\\t\", model.state_dict()[param_tensor].size())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TNyglWkRQTXu",
        "outputId": "36326864-dfab-4be2-acbe-220c24abceb0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model's state_dict:\n",
            "conv1.weight \t torch.Size([6, 3, 5, 5])\n",
            "conv1.bias \t torch.Size([6])\n",
            "conv2.weight \t torch.Size([16, 6, 5, 5])\n",
            "conv2.bias \t torch.Size([16])\n",
            "fc1.weight \t torch.Size([120, 400])\n",
            "fc1.bias \t torch.Size([120])\n",
            "fc2.weight \t torch.Size([84, 120])\n",
            "fc2.bias \t torch.Size([84])\n",
            "fc3.weight \t torch.Size([10, 84])\n",
            "fc3.bias \t torch.Size([10])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Exporting Model on ONNX Format"
      ],
      "metadata": {
        "id": "74_NRkWC1J_N"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torchvision\n",
        "\n",
        "dummy_input = torch.randn(1, 3, 224, 224)\n",
        "model = torchvision.models.alexnet(pretrained=True)\n",
        "torch.onnx.export(model, dummy_input, \"model.onnx\")\n"
      ],
      "metadata": {
        "id": "JhV2scSdQhPQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2d56dde3-5cc2-4697-f5f4-c421c0daf860"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "============= Diagnostic Run torch.onnx.export version 2.0.1+cu118 =============\n",
            "verbose: False, log level: Level.ERROR\n",
            "======================= 0 NONE 0 NOTE 0 WARNING 0 ERROR ========================\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import onnxruntime\n",
        "import numpy as np\n",
        "\n",
        "ort_session = onnxruntime.InferenceSession('model.onnx')\n",
        "\n",
        "# compute ONNX Runtime output prediction\n",
        "ort_inputs = {ort_session.get_inputs()[0].name: np.random.randn(1,3,224,224).astype(np.float32)}\n",
        "ort_outs = ort_session.run(None, ort_inputs)\n",
        "\n"
      ],
      "metadata": {
        "id": "A1TgLZV21NP_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Exporting Transformers Model to ONNX format"
      ],
      "metadata": {
        "id": "taE_YtcK3oh7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from optimum.onnxruntime import ORTModelForCausalLM\n",
        "from transformers import AutoTokenizer\n",
        "\n",
        "model_checkpoint = \"gpt2\"\n",
        "save_directory = \"onnx/\"\n",
        "\n",
        "# Load a model from transformers and export it to ONNX\n",
        "ort_model = ORTModelForCausalLM.from_pretrained(model_checkpoint, export=True)\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)\n",
        "\n",
        "# Save the onnx model and tokenizer\n",
        "ort_model.save_pretrained(save_directory)\n",
        "tokenizer.save_pretrained(save_directory)"
      ],
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        },
        "id": "KCIjCuF91rfx",
        "outputId": "43cf79e4-3e8d-4997-c48f-7a994cf0b557"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/665 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2d45079db5c648fd8d17d84e0f967952"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Framework not specified. Using pt to export to ONNX.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading model.safetensors:   0%|          | 0.00/548M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "931d09d9389345b49b08aab3dadb1f9e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)neration_config.json:   0%|          | 0.00/124 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "073c313a6abb4747a095d764da4f693d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "use_past = False is different than use_present_in_outputs = True, the value of use_present_in_outputs value will be used for the outputs.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)olve/main/vocab.json:   0%|          | 0.00/1.04M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "43ad28fdf5f24e7cb7a93690b2dc33fd"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)olve/main/merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2834cb68f45d4339aedf4f67ee2c1dd7"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/1.36M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cd4b338095d947789c4ed9b5afa423af"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Using framework PyTorch: 2.0.1+cu118\n",
            "Overriding 2 configuration item(s)\n",
            "\t- use_cache -> True\n",
            "\t- pad_token_id -> 0\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/models/gpt2/modeling_gpt2.py:807: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n",
            "  if batch_size <= 0:\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "============= Diagnostic Run torch.onnx.export version 2.0.1+cu118 =============\n",
            "verbose: False, log level: Level.ERROR\n",
            "======================= 0 NONE 0 NOTE 0 WARNING 0 ERROR ========================\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Using framework PyTorch: 2.0.1+cu118\n",
            "Overriding 2 configuration item(s)\n",
            "\t- use_cache -> True\n",
            "\t- pad_token_id -> 0\n",
            "Asked a sequence length of 16, but a sequence length of 1 will be used with use_past == True for `input_ids`.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "============= Diagnostic Run torch.onnx.export version 2.0.1+cu118 =============\n",
            "verbose: False, log level: Level.ERROR\n",
            "======================= 0 NONE 0 NOTE 0 WARNING 0 ERROR ========================\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Asked a sequence length of 16, but a sequence length of 1 will be used with use_past == True for `input_ids`.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('onnx/tokenizer_config.json',\n",
              " 'onnx/special_tokens_map.json',\n",
              " 'onnx/vocab.json',\n",
              " 'onnx/merges.txt',\n",
              " 'onnx/added_tokens.json',\n",
              " 'onnx/tokenizer.json')"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Using for Inference"
      ],
      "metadata": {
        "id": "G3MxL-l56Zm0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer, pipeline\n",
        "from optimum.onnxruntime import ORTModelForCausalLM\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"/content/onnx\")\n",
        "model = ORTModelForCausalLM.from_pretrained(\"/content/onnx\")\n",
        "onnx_gen = pipeline(\"text-generation\", model=model, tokenizer=tokenizer)\n",
        "\n",
        "text = \"I Live in New York City. New York City is \"\n",
        "gen = onnx_gen(text)\n",
        "print(gen)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "96oShzUN17HE",
        "outputId": "084e7435-224d-490b-9d10-8fd0eaaecf33"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1369: UserWarning: Using `max_length`'s default (50) to control the generation length. This behaviour is deprecated and will be removed from the config in v5 of Transformers -- we recommend using `max_new_tokens` to control the maximum length of the generation.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[{'generated_text': \"I Live in New York City. New York City is ˜new' because I live in New York, where I live now, I take an apartment in the Bronx, and I'm going to travel all over the world just for this reason.\"}]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import SegformerFeatureExtractor, SegformerForSemanticSegmentation\n",
        "id2label={0: 'background', 1: 'candy', 2: 'egg tart', 3: 'french fries', 4: 'chocolate', 5: 'biscuit', 6: 'popcorn', 7: 'pudding', 8: 'ice cream', 9: 'cheese butter', 10: 'cake', 11: 'wine', 12: 'milkshake', 13: 'coffee', 14: 'juice', 15: 'milk', 16: 'tea', 17: 'almond', 18: 'red beans', 19: 'cashew', 20: 'dried cranberries', 21: 'soy', 22: 'walnut', 23: 'peanut', 24: 'egg', 25: 'apple', 26: 'date', 27: 'apricot', 28: 'avocado', 29: 'banana', 30: 'strawberry', 31: 'cherry', 32: 'blueberry', 33: 'raspberry', 34: 'mango', 35: 'olives', 36: 'peach', 37: 'lemon', 38: 'pear', 39: 'fig', 40: 'pineapple', 41: 'grape', 42: 'kiwi', 43: 'melon', 44: 'orange', 45: 'watermelon', 46: 'steak', 47: 'pork', 48: 'chicken duck', 49: 'sausage', 50: 'fried meat', 51: 'lamb', 52: 'sauce', 53: 'crab', 54: 'fish', 55: 'shellfish', 56: 'shrimp', 57: 'soup', 58: 'bread', 59: 'corn', 60: 'hamburg', 61: 'pizza', 62: ' hanamaki baozi', 63: 'wonton dumplings', 64: 'pasta', 65: 'noodles', 66: 'rice', 67: 'pie', 68: 'tofu', 69: 'eggplant', 70: 'potato', 71: 'garlic', 72: 'cauliflower', 73: 'tomato', 74: 'kelp', 75: 'seaweed', 76: 'spring onion', 77: 'rape', 78: 'ginger', 79: 'okra', 80: 'lettuce', 81: 'pumpkin', 82: 'cucumber', 83: 'white radish', 84: 'carrot', 85: 'asparagus', 86: 'bamboo shoots', 87: 'broccoli', 88: 'celery stick', 89: 'cilantro mint', 90: 'snow peas', 91: ' cabbage', 92: 'bean sprouts', 93: 'onion', 94: 'pepper', 95: 'green beans', 96: 'French beans', 97: 'king oyster mushroom', 98: 'shiitake', 99: 'enoki mushroom', 100: 'oyster mushroom', 101: 'white button mushroom', 102: 'salad', 103: 'other ingredients'}\n",
        "label2id={'background': 0, 'candy': 1, 'egg tart': 2, 'french fries': 3, 'chocolate': 4, 'biscuit': 5, 'popcorn': 6, 'pudding': 7, 'ice cream': 8, 'cheese butter': 9, 'cake': 10, 'wine': 11, 'milkshake': 12, 'coffee': 13, 'juice': 14, 'milk': 15, 'tea': 16, 'almond': 17, 'red beans': 18, 'cashew': 19, 'dried cranberries': 20, 'soy': 21, 'walnut': 22, 'peanut': 23, 'egg': 24, 'apple': 25, 'date': 26, 'apricot': 27, 'avocado': 28, 'banana': 29, 'strawberry': 30, 'cherry': 31, 'blueberry': 32, 'raspberry': 33, 'mango': 34, 'olives': 35, 'peach': 36, 'lemon': 37, 'pear': 38, 'fig': 39, 'pineapple': 40, 'grape': 41, 'kiwi': 42, 'melon': 43, 'orange': 44, 'watermelon': 45, 'steak': 46, 'pork': 47, 'chicken duck': 48, 'sausage': 49, 'fried meat': 50, 'lamb': 51, 'sauce': 52, 'crab': 53, 'fish': 54, 'shellfish': 55, 'shrimp': 56, 'soup': 57, 'bread': 58, 'corn': 59, 'hamburg': 60, 'pizza': 61, ' hanamaki baozi': 62, 'wonton dumplings': 63, 'pasta': 64, 'noodles': 65, 'rice': 66, 'pie': 67, 'tofu': 68, 'eggplant': 69, 'potato': 70, 'garlic': 71, 'cauliflower': 72, 'tomato': 73, 'kelp': 74, 'seaweed': 75, 'spring onion': 76, 'rape': 77, 'ginger': 78, 'okra': 79, 'lettuce': 80, 'pumpkin': 81, 'cucumber': 82, 'white radish': 83, 'carrot': 84, 'asparagus': 85, 'bamboo shoots': 86, 'broccoli': 87, 'celery stick': 88, 'cilantro mint': 89, 'snow peas': 90, ' cabbage': 91, 'bean sprouts': 92, 'onion': 93, 'pepper': 94, 'green beans': 95, 'French beans': 96, 'king oyster mushroom': 97, 'shiitake': 98, 'enoki mushroom': 99, 'oyster mushroom': 100, 'white button mushroom': 101, 'salad': 102, 'other ingredients': 103}\n",
        "feature_extractor = SegformerFeatureExtractor()\n",
        "model = SegformerForSemanticSegmentation.from_pretrained(\n",
        "    \"prem-timsina/segformer-b0-finetuned-food\",\n",
        "    id2label=id2label,\n",
        "    label2id=label2id\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 137,
          "referenced_widgets": [
            "e48debf332f64dd6a337fc57f98a1611",
            "0a4dac36640f4b79a9ce8bd772be8d30",
            "e98d07550de54c1995e7b8bf7f221909",
            "40a8cdda82f7452ea855d6fde0c98fdd",
            "597ce6a25e2f416d90974dc7789546e6",
            "99b32862d6694cbea97290db86c47880",
            "b3acd60f455c46cfadeadc091f10d518",
            "f7cdfe5489ac4d1eae1149fcaf889577",
            "4e7c58059c1248bfae4c425575725511",
            "6d30016b392a4ebbbdb042087b0a1834",
            "ad7359a9c0c0491e8e948e5bc5ce8f32",
            "0ff6df862fa242e9bd0b2d13d81116f8",
            "d1a52c8cc486483f9484fe2289690815",
            "82372643bd064db9b74410063f5102bb",
            "be11add34656492f9a4c5f0d7920c3da",
            "63bbb9bae7a34750a14ef3b58d85c2f1",
            "085a1872c41f4c93a312f7edbb0b8d39",
            "256983c5609047a786de4b99ae24ecb7",
            "6ac1c2dfb1254006bd1e0fbdbb47897a",
            "69ac5ffc13904f2098de73834e1b496f",
            "ba95a7e2277c4c5198d3c6a83aaa15c9",
            "a19386d3c1514191a14f542c17da5d27"
          ]
        },
        "id": "t5Y9jA8L6wyN",
        "outputId": "d9455ed1-1678-41a0-f4e1-750c6cd96353"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/models/segformer/feature_extraction_segformer.py:28: FutureWarning: The class SegformerFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please use SegformerImageProcessor instead.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/5.36k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e48debf332f64dd6a337fc57f98a1611"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/15.0M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0ff6df862fa242e9bd0b2d13d81116f8"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "import torch\n",
        "import numpy as np\n",
        "from PIL import Image\n",
        "from torchvision.transforms.functional import to_pil_image\n",
        "from transformers import SegformerFeatureExtractor\n",
        "from matplotlib import pyplot as plt\n",
        "from PIL import Image\n",
        "import requests\n",
        "from io import BytesIO\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "# Load the feature extractor\n",
        "url = 'https://raw.githubusercontent.com/bpbpublications/Building-Transformer-Models-with-PyTorch/main/chapter8/food_image.jpg'\n",
        "response = requests.get(url)\n",
        "image = Image.open(BytesIO(response.content)).convert(\"RGB\")\n",
        "\n",
        "inputs = feature_extractor(images=[image], return_tensors=\"pt\")\n",
        "with torch.no_grad():\n",
        "    outputs = model(**inputs)\n",
        "    predictions = outputs.logits.argmax(dim=1).squeeze().cpu().numpy()\n",
        "grayscale_map = np.zeros((predictions.shape[0], predictions.shape[1]), dtype=np.uint8)\n",
        "for label_id in id2label.keys():\n",
        "    grayscale_map[predictions == label_id] = label_id\n",
        "\n",
        "# Convert the grayscale map to a PIL image\n",
        "segmentation_image = Image.fromarray(grayscale_map, mode='L')\n"
      ],
      "metadata": {
        "id": "dDYJdJVCr5lM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a dictionary for the counts of each class in the image\n",
        "unique, counts = np.unique(predictions, return_counts=True)\n",
        "class_counts = dict(zip(unique, counts))\n",
        "\n",
        "# Map the class IDs back to their names\n",
        "class_counts = {id2label[k]: v for k, v in class_counts.items()}\n",
        "\n",
        "print(class_counts)\n"
      ],
      "metadata": {
        "id": "s9PCawAkvW7V",
        "outputId": "1d6eedc0-5240-416c-857e-e86e02aac69b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'background': 3929, 'chicken duck': 3950, 'corn': 2, 'rice': 6153, 'asparagus': 314, 'cilantro mint': 156, 'French beans': 1880}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Show the image\n",
        "plt.figure(figsize=(10,10))\n",
        "plt.imshow(segmentation_image, cmap='gray')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 853
        },
        "id": "_7doHfHWtIpP",
        "outputId": "b99f9d89-355d-4861-d9c2-9d24f7633bdc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "mezSdhEHuWXN"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}